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Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process
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作者 Qixin Lan Binqiang Chen +1 位作者 Bin Yao Wangpeng He 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第3期2825-2844,共20页
The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the s... The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains. 展开更多
关键词 Multi-working conditions tool wear state recognition unsupervised transfer learning domain adaptation maximum mean discrepancy(MMD)
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Wear State Recognition of Drills Based on K-means Cluster and Radial Basis Function Neural Network 被引量:2
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作者 Xu Yang 《International Journal of Automation and computing》 EI 2010年第3期271-276,共6页
Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps: first, d... Drill wear not only affects the surface smoothness of the hole, but also influences the life of the drill. Drill wear state recognition is important in the manufacturing process, which consists of two steps: first, decomposing cutting torque components from the original signals by wavelet packet decomposition (WPD); second, extracting wavelet coefficients of different wear states (i.e., slight, normal, or severe wear) with signal features adapting to Welch spectrum. Finally, monitoring and recognition of the feature vectors of cutting torque signal are performed by using the K-means cluster and radial basis function neural network (RBFNN). The experiments on different tool wears of the multivariable features reveal that the results of monitoring and recognition are significant and effective. 展开更多
关键词 Drill wear state recognition cutting torque signals wavelet packet decomposition (WPD) Welch spectrum energy K-means cluster radial basis function neural network
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Simultaneous energy harvesting and tribological property improvement 被引量:2
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作者 Xiaofan WANG Jiliang MO +3 位作者 Huajiang OUYANG Zaiyu XIANG Wei CHEN Zhongrong ZHOU 《Friction》 SCIE EI CAS CSCD 2021年第5期1275-1291,共17页
In this study,piezoelectric elements were added to a reciprocating friction test bench to harvest friction‐induced vibration energy.Parameters such as vibration acceleration,noise,and voltage signals of the system we... In this study,piezoelectric elements were added to a reciprocating friction test bench to harvest friction‐induced vibration energy.Parameters such as vibration acceleration,noise,and voltage signals of the system were measured and analyzed.The results show that the piezoelectric elements can not only collect vibration energy but also suppress friction‐induced vibration noise(FIVN).Additionally,the wear of the friction interface was examined via optical microscopy(OM),scanning electron microscopy(SEM),and white‐light interferometry(WLI).The results show that the surface wear state improved because of the reduction of FIVN.In order to analyze the experimental results in detail and explain them reasonably,the experimental phenomena were simulated numerically.Moreover,a simplified two‐degree‐of‐freedom numerical model including the original system and the piezoelectric system was established to qualitatively describe the effects,dynamics,and tribological behaviors of the added piezoelectric elements to the original system. 展开更多
关键词 PIEZOELECTRIC friction‐induced vibration(FIV) energy harvester wear state CONTACT
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